LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification

arXiv:2403.15875v1 Announce Type: cross
Abstract: This study constructs the LanguAge Model with Prompt EngineeRing (LAMPER) framework, designed to systematically evaluate the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts and their integration in zero-shot time series (TS) classification. We deploy LAMPER in experimental assessments using 128 univariate TS datasets sourced from the UCR archive. Our findings indicate that the feature representation capacity of LAMPER is influenced by the maximum input token threshold imposed by PLMs.

abstract adaptability arxiv classification cs.ai cs.cl deploy diverse engineering experimental framework integration language language model language models prompt prompts series study time series type zero-shot